12 citations · 24 across the 5 of their papers we have counts for
6 papers
Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features
Konstandinos Kotsiopoulos, Alexey Miroshnikov, Khashayar Filom +1
In recent years, many Machine Learning (ML) explanation techniques have been designed using ideas from cooperative game theory. These game-theoretic explainers suffer from high com…
On marginal feature attributions of tree-based models
Khashayar Filom, Alexey Miroshnikov, Konstandinos Kotsiopoulos +1
Due to their power and ease of use, tree-based machine learning models, such as random forests and gradient-boosted tree ensembles, have become very popular. To interpret them, loc…
Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics
Alexey Miroshnikov, Konstandinos Kotsiopoulos, Ryan Franks +1
This article is a companion paper to our earlier work Miroshnikov et al. (2021) on fairness interpretability, which introduces bias explanations. In the current work, we propose a…
Stability theory of game-theoretic group feature explanations for machine learning models
Alexey Miroshnikov, Konstandinos Kotsiopoulos, Khashayar Filom +1
In this article, we study feature attributions of Machine Learning (ML) models originating from linear game values and coalitional values defined as operators on appropriate functi…
Wasserstein-based fairness interpretability framework for machine learning models
Alexey Miroshnikov, Konstandinos Kotsiopoulos, Ryan Franks +1
The objective of this article is to introduce a fairness interpretability framework for measuring and explaining the bias in classification and regression models at the level of a…
Asymptotic properties and approximation of Bayesian logspline density estimators for communication-free parallel computing methods
Konstandinos Kotsiopoulos, Alexey Miroshnikov, Erin Conlon
In this article we perform an asymptotic analysis of parallel Bayesian logspline density estimators. Such estimators are useful for the analysis of datasets that are partitioned in…